Create app.py

app.py

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+
+
+import gradio as gr
+import moviepy.editor as mp
+from transformers import pipeline
+import time
+
+# Load models
+whisper = pipeline("automatic-speech-recognition", model="openai/whisper-base")  # Use a smaller model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def process_video(video_path):
+    try:
+        # Extract audio from video
+        start_time = time.time()
+        video_clip = mp.VideoFileClip(video_path)
+        audio_path = "extracted_audio.wav"
+        video_clip.audio.write_audiofile(audio_path, codec='pcm_s16le')
+        print(f"Audio extraction took {time.time() - start_time:.2f} seconds")
+
+        # Transcribe audio to text
+        start_time = time.time()
+        transcription = whisper(audio_path)
+        text = transcription['text']
+        print(f"Transcription took {time.time() - start_time:.2f} seconds")
+        if not text:
+            raise ValueError("Transcription returned empty text.")
+        
+        # Summarize text
+        start_time = time.time()
+        summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
+        summary_text = summary[0]['summary_text']
+        print(f"Summarization took {time.time() - start_time:.2f} seconds")
+        if not summary_text:
+            raise ValueError("Summarization returned empty text.")
+        
+        return text, summary_text
+
+    except Exception as e:
+        return str(e), ""
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summarization")],  # Corrected output specification
+    title="Video Transcription and Summarization",
+    description="Upload a video to extract audio, transcribe it to text, and summarize the content."
+)
+
+iface.launch()